Particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene
Track-before-detect methods operate directly upon raw sensor signals without a separate, explicit detection stage. An efficient implementation of a Bayesian track-before-detect particle filter is described for tracking of a single target in a sequence of images. The filter produces a sample-based representation of the probability density function of the target state from raw pixel levels. An indication of the probability that the target is present is also provided. Spatial differentiation of the pixel array data allows objects to be tracked when viewed against a general scene with additive noise. Simulated results illustrate that a dim point target of unknown amplitude, which has become spatially blurred, may be tracked through a sequence of structured images. Detection sensitivity is established using simulated results. The novel aspect of the work is the efficient implementation – in particular, the calculation of the probability of the target being present.